Overview

Dataset statistics

Number of variables26
Number of observations989617
Missing cells34
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory196.3 MiB
Average record size in memory208.0 B

Variable types

CAT12
NUM12
BOOL2

Reproduction

Analysis started2021-09-06 03:31:40.770106
Analysis finished2021-09-06 03:34:01.963817
Duration2 minutes and 21.19 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

GRID_TYPE has constant value "Shot Chart Detail" Constant
SHOT_ATTEMPTED_FLAG has constant value "1" Constant
PLAYER_NAME has a high cardinality: 539 distinct values High cardinality
ACTION_TYPE has a high cardinality: 53 distinct values High cardinality
PERIOD is highly correlated with GAME_EVENT_IDHigh correlation
GAME_EVENT_ID is highly correlated with PERIODHigh correlation
GAME_DATE is highly correlated with GAME_IDHigh correlation
GAME_ID is highly correlated with GAME_DATEHigh correlation
SHOT_ZONE_BASIC is highly correlated with SHOT_TYPEHigh correlation
SHOT_TYPE is highly correlated with SHOT_ZONE_BASIC and 1 other fieldsHigh correlation
SHOT_ZONE_RANGE is highly correlated with SHOT_TYPEHigh correlation
MINUTES_REMAINING has 95937 (9.7%) zeros Zeros
SECONDS_REMAINING has 26720 (2.7%) zeros Zeros
SHOT_DISTANCE has 91731 (9.3%) zeros Zeros
LOC_X has 49320 (5.0%) zeros Zeros
LOC_Y has 17317 (1.7%) zeros Zeros

Variables

df_index
Real number (ℝ≥0)

Distinct count190983
Unique (%)19.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84251.11526681534
Minimum0
Maximum190982
Zeros6
Zeros (%)< 0.1%
Memory size7.6 MiB
2021-09-06T00:34:02.076797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8246
Q141234
median82468
Q3123702
95-th percentile170023
Maximum190982
Range190982
Interquartile range (IQR)82468

Descriptive statistics

Standard deviation50438.49262
Coefficient of variation (CV)0.5986685454
Kurtosis-1.024681249
Mean84251.11527
Median Absolute Deviation (MAD)41234
Skewness0.1580072179
Sum8.337633594e+10
Variance2544041538
2021-09-06T00:34:02.187354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
20476< 0.1%
 
160736< 0.1%
 
488576< 0.1%
 
468086< 0.1%
 
529516< 0.1%
 
509026< 0.1%
 
570456< 0.1%
 
549966< 0.1%
 
611396< 0.1%
 
590906< 0.1%
 
Other values (190973)989557> 99.9%
 
ValueCountFrequency (%) 
06< 0.1%
 
16< 0.1%
 
26< 0.1%
 
36< 0.1%
 
46< 0.1%
 
ValueCountFrequency (%) 
1909821< 0.1%
 
1909811< 0.1%
 
1909801< 0.1%
 
1909791< 0.1%
 
1909781< 0.1%
 

GRID_TYPE
Categorical

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
Shot Chart Detail
989617
ValueCountFrequency (%) 
Shot Chart Detail989617100.0%
 
2021-09-06T00:34:02.322085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

GAME_ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count7059
Unique (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21774118.457167774
Minimum21500001
Maximum22001080
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2021-09-06T00:34:02.424626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum21500001
5-th percentile21500493
Q121601040
median21800424
Q321900729
95-th percentile22000802
Maximum22001080
Range501079
Interquartile range (IQR)299689

Descriptive statistics

Standard deviation166181.2229
Coefficient of variation (CV)0.007632052853
Kurtosis-1.173328214
Mean21774118.46
Median Absolute Deviation (MAD)100435
Skewness-0.1712635354
Sum2.154803779e+13
Variance2.761619884e+10
2021-09-06T00:34:02.522807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
21800639216< 0.1%
 
22000216215< 0.1%
 
21800928214< 0.1%
 
22000811214< 0.1%
 
22001012214< 0.1%
 
21900818213< 0.1%
 
21800881213< 0.1%
 
21901281213< 0.1%
 
21900787213< 0.1%
 
22000919212< 0.1%
 
Other values (7049)98748099.8%
 
ValueCountFrequency (%) 
21500001150< 0.1%
 
21500002112< 0.1%
 
21500003103< 0.1%
 
21500004130< 0.1%
 
2150000597< 0.1%
 
ValueCountFrequency (%) 
22001080185< 0.1%
 
22001079192< 0.1%
 
22001078202< 0.1%
 
22001077176< 0.1%
 
22001076185< 0.1%
 

GAME_EVENT_ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count897
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean303.45167878078087
Minimum2
Maximum1012
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2021-09-06T00:34:02.631212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile24
Q1138
median298
Q3454
95-th percentile617
Maximum1012
Range1010
Interquartile range (IQR)316

Descriptive statistics

Standard deviation189.2898542
Coefficient of variation (CV)0.6237891151
Kurtosis-1.016659769
Mean303.4516788
Median Absolute Deviation (MAD)158
Skewness0.1698862561
Sum300300940
Variance35830.6489
2021-09-06T00:34:02.722302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
741610.4%
 
933220.3%
 
1128510.3%
 
1326670.3%
 
1525390.3%
 
1724530.2%
 
1924340.2%
 
1624130.2%
 
2224120.2%
 
2523970.2%
 
Other values (887)96196897.2%
 
ValueCountFrequency (%) 
213560.1%
 
37340.1%
 
49680.1%
 
58520.1%
 
68640.1%
 
ValueCountFrequency (%) 
10121< 0.1%
 
9861< 0.1%
 
9841< 0.1%
 
9801< 0.1%
 
9791< 0.1%
 

PLAYER_ID
Real number (ℝ≥0)

Distinct count539
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean696610.8768846937
Minimum2544
Maximum1630466
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2021-09-06T00:34:02.826058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2544
5-th percentile200755
Q1202339
median203500
Q31627741
95-th percentile1629164
Maximum1630466
Range1627922
Interquartile range (IQR)1425402

Descriptive statistics

Standard deviation685022.08
Coefficient of variation (CV)0.9833640311
Kurtosis-1.605058913
Mean696610.8769
Median Absolute Deviation (MAD)1891
Skewness0.6194485052
Sum6.893779662e+11
Variance4.692552501e+11
2021-09-06T00:34:02.921328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
20156690640.9%
 
20193587540.9%
 
20308185920.9%
 
20307878960.8%
 
20346878600.8%
 
20194276160.8%
 
254475590.8%
 
20233174790.8%
 
20395274090.7%
 
20268973920.7%
 
Other values (529)90999692.0%
 
ValueCountFrequency (%) 
254475590.8%
 
254655440.6%
 
2617145< 0.1%
 
273027730.3%
 
273817410.2%
 
ValueCountFrequency (%) 
163046667< 0.1%
 
163027384< 0.1%
 
1630271108< 0.1%
 
16302687< 0.1%
 
1630267315< 0.1%
 

PLAYER_NAME
Categorical

HIGH CARDINALITY

Distinct count539
Unique (%)0.1%
Missing34
Missing (%)< 0.1%
Memory size7.6 MiB
Russell Westbrook
 
9064
James Harden
 
8754
Damian Lillard
 
8592
Bradley Beal
 
7896
CJ McCollum
 
7860
Other values (534)
947417
ValueCountFrequency (%) 
Russell Westbrook90640.9%
 
James Harden87540.9%
 
Damian Lillard85920.9%
 
Bradley Beal78960.8%
 
CJ McCollum78600.8%
 
DeMar DeRozan76160.8%
 
LeBron James75590.8%
 
Paul George74790.8%
 
Andrew Wiggins74090.7%
 
Kemba Walker73920.7%
 
Other values (529)90996292.0%
 
2021-09-06T00:34:03.059961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length24
Median length13
Mean length13.16011346
Min length3

TEAM_ID
Real number (ℝ≥0)

Distinct count30
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1610612751.6020148
Minimum1610612737
Maximum1610612766
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2021-09-06T00:34:03.164129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1610612737
5-th percentile1610612738
Q11610612744
median1610612752
Q31610612759
95-th percentile1610612765
Maximum1610612766
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.705218982
Coefficient of variation (CV)5.404911251e-09
Kurtosis-1.21511966
Mean1610612752
Median Absolute Deviation (MAD)8
Skewness-0.02874163379
Sum1.593889759e+15
Variance75.78083752
2021-09-06T00:34:03.259787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1610612765380063.8%
 
1610612761375003.8%
 
1610612760370823.7%
 
1610612738368593.7%
 
1610612757360973.6%
 
1610612750358923.6%
 
1610612762349033.5%
 
1610612764344623.5%
 
1610612747341653.5%
 
1610612755340833.4%
 
Other values (20)63056863.7%
 
ValueCountFrequency (%) 
1610612737333503.4%
 
1610612738368593.7%
 
1610612739316533.2%
 
1610612740330863.3%
 
1610612741336793.4%
 
ValueCountFrequency (%) 
1610612766309453.1%
 
1610612765380063.8%
 
1610612764344623.5%
 
1610612763266462.7%
 
1610612762349033.5%
 

TEAM_NAME
Categorical

Distinct count30
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
Detroit Pistons
 
38006
Toronto Raptors
 
37500
Oklahoma City Thunder
 
37082
Boston Celtics
 
36859
Portland Trail Blazers
 
36097
Other values (25)
804073
ValueCountFrequency (%) 
Detroit Pistons380063.8%
 
Toronto Raptors375003.8%
 
Oklahoma City Thunder370823.7%
 
Boston Celtics368593.7%
 
Portland Trail Blazers360973.6%
 
Minnesota Timberwolves358923.6%
 
Utah Jazz349033.5%
 
Washington Wizards344623.5%
 
Los Angeles Lakers341653.5%
 
Philadelphia 76ers340833.4%
 
Other values (20)63056863.7%
 
2021-09-06T00:34:03.413734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length22
Median length15
Mean length15.78954282
Min length9

PERIOD
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count8
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4672221677679347
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2021-09-06T00:34:03.539909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.134206885
Coefficient of variation (CV)0.459710074
Kurtosis-1.209259186
Mean2.467222168
Median Absolute Deviation (MAD)1
Skewness0.09916322205
Sum2441605
Variance1.286425259
2021-09-06T00:34:03.640518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
126276626.6%
 
324958225.2%
 
224336224.6%
 
422714523.0%
 
559680.6%
 
66530.1%
 
797< 0.1%
 
844< 0.1%
 
ValueCountFrequency (%) 
126276626.6%
 
224336224.6%
 
324958225.2%
 
422714523.0%
 
559680.6%
 
ValueCountFrequency (%) 
844< 0.1%
 
797< 0.1%
 
66530.1%
 
559680.6%
 
422714523.0%
 

MINUTES_REMAINING
Real number (ℝ≥0)

ZEROS

Distinct count13
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.350665964711601
Minimum0
Maximum12
Zeros95937
Zeros (%)9.7%
Memory size7.6 MiB
2021-09-06T00:34:03.751832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum12
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.42966824
Coefficient of variation (CV)0.6409796953
Kurtosis-1.203374794
Mean5.350665965
Median Absolute Deviation (MAD)3
Skewness0.0142333535
Sum5295110
Variance11.76262423
2021-09-06T00:34:03.846570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0959379.7%
 
4844678.5%
 
7837658.5%
 
6837388.5%
 
5836758.5%
 
8829968.4%
 
3829818.4%
 
9825828.3%
 
10819848.3%
 
1805808.1%
 
Other values (3)14691214.8%
 
ValueCountFrequency (%) 
0959379.7%
 
1805808.1%
 
2802818.1%
 
3829818.4%
 
4844678.5%
 
ValueCountFrequency (%) 
1212< 0.1%
 
11666196.7%
 
10819848.3%
 
9825828.3%
 
8829968.4%
 

SECONDS_REMAINING
Real number (ℝ≥0)

ZEROS

Distinct count60
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.862704460412463
Minimum0
Maximum59
Zeros26720
Zeros (%)2.7%
Memory size7.6 MiB
2021-09-06T00:34:03.958390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q114
median29
Q344
95-th percentile56
Maximum59
Range59
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.4310755
Coefficient of variation (CV)0.6039307759
Kurtosis-1.194178162
Mean28.86270446
Median Absolute Deviation (MAD)15
Skewness0.005612559274
Sum28563023
Variance303.8423931
2021-09-06T00:34:04.059516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0267202.7%
 
1194962.0%
 
2178001.8%
 
3170691.7%
 
45169761.7%
 
43169051.7%
 
46168881.7%
 
41168741.7%
 
4168631.7%
 
30168611.7%
 
Other values (50)80716581.6%
 
ValueCountFrequency (%) 
0267202.7%
 
1194962.0%
 
2178001.8%
 
3170691.7%
 
4168631.7%
 
ValueCountFrequency (%) 
59153991.6%
 
58151881.5%
 
57150241.5%
 
56153391.5%
 
55150671.5%
 

EVENT_TYPE
Categorical

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
Missed Shot
531043
Made Shot
458574
ValueCountFrequency (%) 
Missed Shot53104353.7%
 
Made Shot45857446.3%
 
2021-09-06T00:34:04.206173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.07322934
Min length9

ACTION_TYPE
Categorical

HIGH CARDINALITY

Distinct count53
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
Jump Shot
375167
Pullup Jump shot
97142
Driving Layup Shot
85252
Layup Shot
 
57045
Step Back Jump shot
 
33927
Other values (48)
341084
ValueCountFrequency (%) 
Jump Shot37516737.9%
 
Pullup Jump shot971429.8%
 
Driving Layup Shot852528.6%
 
Layup Shot570455.8%
 
Step Back Jump shot339273.4%
 
Driving Floating Jump Shot301633.0%
 
Floating Jump shot235132.4%
 
Cutting Layup Shot214642.2%
 
Running Layup Shot206972.1%
 
Tip Layup Shot204312.1%
 
Other values (43)22481622.7%
 
2021-09-06T00:34:04.345134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length34
Median length16
Mean length14.51304394
Min length7

SHOT_TYPE
Categorical

HIGH CORRELATION

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
2PT Field Goal
648034
3PT Field Goal
341583
ValueCountFrequency (%) 
2PT Field Goal64803465.5%
 
3PT Field Goal34158334.5%
 
2021-09-06T00:34:04.488750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

SHOT_ZONE_BASIC
Categorical

HIGH CORRELATION

Distinct count7
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
Restricted Area
319872
Above the Break 3
262007
Mid-Range
169567
In The Paint (Non-RA)
158879
Left Corner 3
 
39826
Other values (2)
 
39466
ValueCountFrequency (%) 
Restricted Area31987232.3%
 
Above the Break 326200726.5%
 
Mid-Range16956717.1%
 
In The Paint (Non-RA)15887916.1%
 
Left Corner 3398264.0%
 
Right Corner 3374763.8%
 
Backcourt19900.2%
 
2021-09-06T00:34:04.626401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length21
Median length15
Mean length15.33428892
Min length9

SHOT_ZONE_AREA
Categorical

Distinct count6
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
Center(C)
566715
Left Side Center(LC)
123686
Right Side Center(RC)
120269
Left Side(L)
 
90332
Right Side(R)
 
86409
ValueCountFrequency (%) 
Center(C)56671557.3%
 
Left Side Center(LC)12368612.5%
 
Right Side Center(RC)12026912.2%
 
Left Side(L)903329.1%
 
Right Side(R)864098.7%
 
Back Court(BC)22060.2%
 
2021-09-06T00:34:04.757447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length21
Median length9
Mean length12.46743841
Min length9

SHOT_ZONE_RANGE
Categorical

HIGH CORRELATION

Distinct count5
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
Less Than 8 ft.
411948
24+ ft.
339093
8-16 ft.
133184
16-24 ft.
103186
Back Court Shot
 
2206
ValueCountFrequency (%) 
Less Than 8 ft.41194841.6%
 
24+ ft.33909334.3%
 
8-16 ft.13318413.5%
 
16-24 ft.10318610.4%
 
Back Court Shot22060.2%
 
2021-09-06T00:34:04.900353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length15
Median length9
Mean length10.69111282
Min length7

SHOT_DISTANCE
Real number (ℝ≥0)

ZEROS

Distinct count88
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.99932398089362
Minimum0
Maximum87
Zeros91731
Zeros (%)9.3%
Memory size7.6 MiB
2021-09-06T00:34:05.197323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median12
Q324
95-th percentile26
Maximum87
Range87
Interquartile range (IQR)22

Descriptive statistics

Standard deviation10.43408666
Coefficient of variation (CV)0.8026637901
Kurtosis-0.9445894518
Mean12.99932398
Median Absolute Deviation (MAD)11
Skewness0.2093632493
Sum12864352
Variance108.8701643
2021-09-06T00:34:05.313476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
111291311.4%
 
2510368810.5%
 
0917319.3%
 
2776337.8%
 
24658146.7%
 
26593796.0%
 
23456844.6%
 
3375953.8%
 
22280382.8%
 
4263412.7%
 
Other values (78)34080134.4%
 
ValueCountFrequency (%) 
0917319.3%
 
111291311.4%
 
2776337.8%
 
3375953.8%
 
4263412.7%
 
ValueCountFrequency (%) 
872< 0.1%
 
864< 0.1%
 
852< 0.1%
 
846< 0.1%
 
837< 0.1%
 

LOC_X
Real number (ℝ)

ZEROS

Distinct count501
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.319432669406447
Minimum-250
Maximum250
Zeros49320
Zeros (%)5.0%
Memory size7.6 MiB
2021-09-06T00:34:05.416200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-250
5-th percentile-212
Q1-46
median0
Q343
95-th percentile207
Maximum250
Range500
Interquartile range (IQR)89

Descriptive statistics

Standard deviation108.3561582
Coefficient of variation (CV)-82.12329487
Kurtosis0.07985736297
Mean-1.319432669
Median Absolute Deviation (MAD)45
Skewness-0.02168937212
Sum-1305733
Variance11741.05701
2021-09-06T00:34:05.516931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0493205.0%
 
2150161.5%
 
-2148621.5%
 
9137771.4%
 
6121891.2%
 
-6118951.2%
 
3117301.2%
 
-3111611.1%
 
-9101651.0%
 
199131.0%
 
Other values (491)82958983.8%
 
ValueCountFrequency (%) 
-25061< 0.1%
 
-2496< 0.1%
 
-248113< 0.1%
 
-24723< 0.1%
 
-246179< 0.1%
 
ValueCountFrequency (%) 
25037< 0.1%
 
2497< 0.1%
 
24861< 0.1%
 
24720< 0.1%
 
246142< 0.1%
 

LOC_Y
Real number (ℝ)

ZEROS

Distinct count837
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.18057996174278
Minimum-52
Maximum867
Zeros17317
Zeros (%)1.7%
Memory size7.6 MiB
2021-09-06T00:34:05.624162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-52
5-th percentile-3
Q111
median51
Q3175
95-th percentile253
Maximum867
Range919
Interquartile range (IQR)164

Descriptive statistics

Standard deviation94.01477606
Coefficient of variation (CV)1.019897858
Kurtosis0.2173283998
Mean92.18057996
Median Absolute Deviation (MAD)50
Skewness0.8209599721
Sum91223469
Variance8838.778118
2021-09-06T00:34:05.717916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
11247312.5%
 
1229262.3%
 
-6200712.0%
 
8198452.0%
 
7196112.0%
 
16193532.0%
 
13179161.8%
 
0173171.7%
 
2164491.7%
 
-1142351.4%
 
Other values (827)79716380.6%
 
ValueCountFrequency (%) 
-521< 0.1%
 
-513< 0.1%
 
-492< 0.1%
 
-481< 0.1%
 
-466< 0.1%
 
ValueCountFrequency (%) 
8671< 0.1%
 
8571< 0.1%
 
8551< 0.1%
 
8522< 0.1%
 
8501< 0.1%
 

SHOT_ATTEMPTED_FLAG
Boolean

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
1
989617
ValueCountFrequency (%) 
1989617100.0%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
0
531043
1
458574
ValueCountFrequency (%) 
053104353.7%
 
145857446.3%
 

GAME_DATE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count949
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20184303.72078693
Minimum20151027
Maximum20210516
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2021-09-06T00:34:05.826552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum20151027
5-th percentile20160101
Q120170319
median20181214
Q320200201
95-th percentile20210412
Maximum20210516
Range59489
Interquartile range (IQR)29882

Descriptive statistics

Standard deviation17894.21495
Coefficient of variation (CV)0.0008865411061
Kurtosis-1.066416694
Mean20184303.72
Median Absolute Deviation (MAD)11083
Skewness-0.05932316428
Sum1.99747301e+13
Variance320202928.5
2021-09-06T00:34:05.928779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2021051627020.3%
 
2020012024390.2%
 
2019122823740.2%
 
2019112723470.2%
 
2019112921820.2%
 
2018112321760.2%
 
2021012721520.2%
 
2020122321470.2%
 
2021041421470.2%
 
2021042121280.2%
 
Other values (939)96682397.7%
 
ValueCountFrequency (%) 
20151027365< 0.1%
 
2015102813690.1%
 
20151029240< 0.1%
 
2015103013400.1%
 
201510316280.1%
 
ValueCountFrequency (%) 
2021051627020.3%
 
2021051511040.1%
 
2021051414150.1%
 
2021051315910.2%
 
2021051210830.1%
 

HTM
Categorical

Distinct count30
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
OKC
 
35389
TOR
 
35239
DET
 
35116
BOS
 
34972
POR
 
34742
Other values (25)
814159
ValueCountFrequency (%) 
OKC353893.6%
 
TOR352393.6%
 
DET351163.5%
 
BOS349723.5%
 
POR347423.5%
 
WAS339133.4%
 
MIN339043.4%
 
NOP337173.4%
 
PHI337023.4%
 
ATL336743.4%
 
Other values (20)64524965.2%
 
2021-09-06T00:34:06.079381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

VTM
Categorical

Distinct count30
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
OKC
 
35160
POR
 
35081
TOR
 
34745
BOS
 
34725
DET
 
34629
Other values (25)
815277
ValueCountFrequency (%) 
OKC351603.6%
 
POR350813.5%
 
TOR347453.5%
 
BOS347253.5%
 
DET346293.5%
 
MIN342413.5%
 
UTA341583.5%
 
WAS341343.4%
 
LAL339423.4%
 
MIL339243.4%
 
Other values (20)64487865.2%
 
2021-09-06T00:34:06.220006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

SEASON_ID
Categorical

Distinct count6
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
2020-21
190983
2018-19
188656
2019-20
179913
2017-18
160274
2016-17
145805
ValueCountFrequency (%) 
2020-2119098319.3%
 
2018-1918865619.1%
 
2019-2017991318.2%
 
2017-1816027416.2%
 
2016-1714580514.7%
 
2015-1612398612.5%
 
2021-09-06T00:34:06.340685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Interactions

2021-09-06T00:32:43.554367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:44.074473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:44.556384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:45.061127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:45.621753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:46.080851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:46.561817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:47.115656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:47.605382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:48.102576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:48.575964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:49.037700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:49.548498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:50.050675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:50.538300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:51.007980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:51.498661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:51.964399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:52.432114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:52.909478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:53.385312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:53.865073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:54.368751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:54.840437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:55.339338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:55.840057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:56.304345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:57.053467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:57.587273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:58.044092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:58.494920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:58.949651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:59.406554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:32:59.913683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:00.379683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:00.868124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:01.323276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:01.837603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:02.353200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:02.838363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:03.360432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:03.812787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:04.288954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:04.759910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:05.244823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:05.778308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:06.286742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:06.739697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:07.221988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:07.759243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:08.234821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:08.707278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:09.199509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:09.721887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:10.278853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:10.804746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:11.338382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:11.855127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:12.400814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:12.893314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:13.362338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:13.817072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:14.274283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:14.729665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:15.192144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:15.617707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:16.143192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:16.727018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:17.177058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:17.640084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:18.165326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:18.604972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:19.076572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:19.531096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:19.984915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:20.479303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:20.949482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:21.401850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:21.844968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:22.309351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:22.782515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:23.254016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:23.726800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:24.189179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:24.649923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:25.135261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:25.597751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:26.080411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:26.587437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:27.032200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:27.490581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:27.940061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:28.448681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:28.909024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:29.355876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:29.811131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:30.270290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:30.760791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:31.238563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:31.685691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:32.152992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:32.596174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:33.064044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:33.509596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:33.974403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:34.429849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:34.867668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:35.317615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:35.797287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:36.303190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:36.826148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:37.280268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:37.749351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:38.209164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:38.716373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:39.200772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:39.661530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:40.148401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:40.746250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:41.239754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:41.693137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:42.149766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:42.601221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:43.118340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:43.621069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:44.113226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:44.576080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:45.103209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:45.626992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:46.142099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:46.633576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:47.146811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:47.613052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:48.098726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:48.569743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:49.096306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:49.567634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:50.015558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:50.475790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:50.942814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:51.445440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:51.938424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:52.403480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:52.849041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-09-06T00:34:06.440367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-06T00:34:06.678048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-06T00:34:06.912423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-06T00:34:07.250718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-06T00:34:07.618264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-06T00:33:54.678064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:33:57.369055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T00:34:00.601731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexGRID_TYPEGAME_IDGAME_EVENT_IDPLAYER_IDPLAYER_NAMETEAM_IDTEAM_NAMEPERIODMINUTES_REMAININGSECONDS_REMAININGEVENT_TYPEACTION_TYPESHOT_TYPESHOT_ZONE_BASICSHOT_ZONE_AREASHOT_ZONE_RANGESHOT_DISTANCELOC_XLOC_YSHOT_ATTEMPTED_FLAGSHOT_MADE_FLAGGAME_DATEHTMVTMSEASON_ID
00Shot Chart Detail2200001227203932Aaron Gordon1610612753Orlando Magic1956Missed ShotJump Shot3PT Field GoalAbove the Break 3Left Side Center(LC)24+ ft.24-1461991020201223ORLMIA2020-21
11Shot Chart Detail2200001240203932Aaron Gordon1610612753Orlando Magic1855Made ShotRunning Dunk Shot2PT Field GoalRestricted AreaCenter(C)Less Than 8 ft.0-451120201223ORLMIA2020-21
22Shot Chart Detail2200001260203932Aaron Gordon1610612753Orlando Magic1710Missed ShotStep Back Jump shot3PT Field GoalAbove the Break 3Left Side Center(LC)24+ ft.25-1541981020201223ORLMIA2020-21
33Shot Chart Detail2200001264203932Aaron Gordon1610612753Orlando Magic1634Made ShotDunk Shot2PT Field GoalRestricted AreaCenter(C)Less Than 8 ft.0-4-41120201223ORLMIA2020-21
44Shot Chart Detail2200001275203932Aaron Gordon1610612753Orlando Magic1536Made ShotTip Layup Shot2PT Field GoalRestricted AreaCenter(C)Less Than 8 ft.0001120201223ORLMIA2020-21
55Shot Chart Detail22000012183203932Aaron Gordon1610612753Orlando Magic2950Missed ShotStep Back Jump shot3PT Field GoalAbove the Break 3Left Side Center(LC)24+ ft.25-1871711020201223ORLMIA2020-21
66Shot Chart Detail22000012249203932Aaron Gordon1610612753Orlando Magic2551Made ShotLayup Shot2PT Field GoalRestricted AreaCenter(C)Less Than 8 ft.2-4271120201223ORLMIA2020-21
77Shot Chart Detail22000012515203932Aaron Gordon1610612753Orlando Magic41134Made ShotJump Shot3PT Field GoalRight Corner 3Right Side(R)24+ ft.23230411120201223ORLMIA2020-21
88Shot Chart Detail22000012539203932Aaron Gordon1610612753Orlando Magic4941Made ShotJump Shot2PT Field GoalMid-RangeRight Side(R)16-24 ft.19167941120201223ORLMIA2020-21
99Shot Chart Detail22000012557203932Aaron Gordon1610612753Orlando Magic4833Made ShotRunning Alley Oop Dunk Shot2PT Field GoalRestricted AreaCenter(C)Less Than 8 ft.26261120201223ORLMIA2020-21

Last rows

df_indexGRID_TYPEGAME_IDGAME_EVENT_IDPLAYER_IDPLAYER_NAMETEAM_IDTEAM_NAMEPERIODMINUTES_REMAININGSECONDS_REMAININGEVENT_TYPEACTION_TYPESHOT_TYPESHOT_ZONE_BASICSHOT_ZONE_AREASHOT_ZONE_RANGESHOT_DISTANCELOC_XLOC_YSHOT_ATTEMPTED_FLAGSHOT_MADE_FLAGGAME_DATEHTMVTMSEASON_ID
989607123976Shot Chart Detail21501226161203897Zach LaVine1610612750Minnesota Timberwolves287Made ShotAlley Oop Dunk Shot2PT Field GoalRestricted AreaCenter(C)Less Than 8 ft.0011120160413MINNOP2015-16
989608123977Shot Chart Detail21501226193203897Zach LaVine1610612750Minnesota Timberwolves2418Missed ShotJump Shot3PT Field GoalRight Corner 3Right Side(R)24+ ft.2222731020160413MINNOP2015-16
989609123978Shot Chart Detail21501226253203897Zach LaVine1610612750Minnesota Timberwolves31051Missed ShotPullup Jump shot3PT Field GoalAbove the Break 3Center(C)24+ ft.24712341020160413MINNOP2015-16
989610123979Shot Chart Detail21501226256203897Zach LaVine1610612750Minnesota Timberwolves31039Made ShotRunning Layup Shot2PT Field GoalRestricted AreaCenter(C)Less Than 8 ft.112111120160413MINNOP2015-16
989611123980Shot Chart Detail21501226260203897Zach LaVine1610612750Minnesota Timberwolves3104Made ShotStep Back Jump shot3PT Field GoalAbove the Break 3Center(C)24+ ft.24242441120160413MINNOP2015-16
989612123981Shot Chart Detail21501226270203897Zach LaVine1610612750Minnesota Timberwolves3859Missed ShotTurnaround Jump Shot2PT Field GoalMid-RangeLeft Side(L)16-24 ft.16-158511020160413MINNOP2015-16
989613123982Shot Chart Detail21501226308203897Zach LaVine1610612750Minnesota Timberwolves3523Missed ShotJump Shot3PT Field GoalAbove the Break 3Right Side Center(RC)24+ ft.24872281020160413MINNOP2015-16
989614123983Shot Chart Detail21501226311203897Zach LaVine1610612750Minnesota Timberwolves358Missed ShotDriving Reverse Layup Shot2PT Field GoalRestricted AreaCenter(C)Less Than 8 ft.0431020160413MINNOP2015-16
989615123984Shot Chart Detail21501226326203897Zach LaVine1610612750Minnesota Timberwolves3347Missed ShotJump Shot3PT Field GoalAbove the Break 3Right Side Center(RC)24+ ft.24812321020160413MINNOP2015-16
989616123985Shot Chart Detail21501226329203897Zach LaVine1610612750Minnesota Timberwolves3324Made ShotDunk Shot2PT Field GoalRestricted AreaCenter(C)Less Than 8 ft.0011120160413MINNOP2015-16